{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/117741"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/117741","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Machine learning for digital health: Transforming clinical data into knowledge","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","abstract_has_math":false,"creators":["Kaur, Rachneet"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Industrial Engineering","degree_department":null,"school":null,"contributors":["Sowers, Richard B.","Beck, Carolyn L.","Kesavadas, Thenkurussi","Hernandez, Manuel E."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12","date_published":"2022-12","updated_at":"2026-07-22T22:24:56Z","subjects":["Artificial Intelligence","Deep Learning","Health Analytics","Gait","Vision","Multiple Sclerosis","Alzheimer's Disease","Parkinson's Disease"],"languages":["en","eng"],"rights":["Copyright 2022 Rachneet Kaur"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/117741","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sowers, Richard B.","Beck, Carolyn L.","Kesavadas, Thenkurussi","Hernandez, Manuel E."]},{"key":"dc:creator","label":"Author","values":["Kaur, Rachneet"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-12","2022-12-02"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Artificial Intelligence","Deep Learning","Health Analytics","Gait","Vision","Multiple Sclerosis","Alzheimer's Disease","Parkinson's Disease"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Rachneet Kaur"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/117741"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","The student, Rachneet Kaur, accepted the attached license on 2022-12-01 at 12:06.","The student, Rachneet Kaur, submitted this Dissertation for approval on 2022-12-01 at 12:23.","This Dissertation was approved for publication on 2022-12-02 at 10:49.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18536 on 2023-04-12 at 07:24:34","Promoting well being and healthy aging in older adults is becoming increasingly crucial to advance towards a better quality of life. In an effort towards the same, we propose some current and novel approaches to the study of neurological disorders in this thesis. Broadly, we attempt to address recurring problems in neurological disorders, namely early-stage disease diagnosis and progression prediction. Diagnosing neurological conditions is difficult, especially in the early stages, many individuals go undiagnosed partly due to the complex heterogeneity in disease progression. Thus, we study the integration of artificial intelligence (AI) and health data that we believe may provide a viable patient-centric approach to aid clinicians in designing novel AI-based disease prediction strategies and monitoring disease progression. Our ultimate objective is to facilitate the future developments of AI in digital healthcare. This work proposes new data-driven machine learning-based solutions utilizing health data from multiple modalities, such as gait (e.g., spatiotemporal gait metrics, acceleration, ground reaction forces), cognitive, functional, and longitudinal clinical assessments, and neural responses measured via electrophysiological (e.g., electromyography (EMG), electrocardiography (ECG)) signals, to improve early disease prediction and progression in neurological movement disorders. We measure our ability to use these signals to classify disability and predict progression of cognitive and motor changes in persons with neuromuscular disorders. This thesis is a multidisciplinary effort that involves novel combinations of sensors, vision, machine learning, bio-mechanics, and dynamical analyses to better characterize neurological disorders. These studies on the integration of AI and health data may provide a viable patient-centric approach to aid clinicians in designing novel AI-based disease prediction strategies and monitoring disease progression. This may help providers to individualize treatment plans and design improved clinical trials; thus, help reduce the skyrocketing healthcare costs in the future. The focus of this dissertation is on the following three areas under the broad umbrella of AI for digital healthcare: 1) Gait analysis for differentiation of neurological disorders, where we focus on disease diagnosis and study gait data-driven methodologies for an automated quantification of neurological gait disorders, such as multiple sclerosis and Parkinson's disease, 2) Clinical data analysis for prediction of disease progression, where we focus on early-stage disease progression prediction and propose machine learning models to identify etiological disease subtypes and study trajectory progression in Alzheimer’s disease, and 3) Virtual reality for analyzing neural responses to anxiety, where we examine the potential of virtual reality neurorehabilitation for ameliorating fall-related anxiety in adults."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Machine learning for digital health: Transforming clinical data into knowledge"]}]}],"canonical_facts":{"dc:contributor":["Sowers, Richard B.","Beck, Carolyn L.","Kesavadas, Thenkurussi","Hernandez, Manuel E."],"dc:creator":["Kaur, Rachneet"],"dc:date":["2022-12","2022-12-02"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","The student, Rachneet Kaur, accepted the attached license on 2022-12-01 at 12:06.","The student, Rachneet Kaur, submitted this Dissertation for approval on 2022-12-01 at 12:23.","This Dissertation was approved for publication on 2022-12-02 at 10:49.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18536 on 2023-04-12 at 07:24:34","Promoting well being and healthy aging in older adults is becoming increasingly crucial to advance towards a better quality of life. In an effort towards the same, we propose some current and novel approaches to the study of neurological disorders in this thesis. Broadly, we attempt to address recurring problems in neurological disorders, namely early-stage disease diagnosis and progression prediction. Diagnosing neurological conditions is difficult, especially in the early stages, many individuals go undiagnosed partly due to the complex heterogeneity in disease progression. Thus, we study the integration of artificial intelligence (AI) and health data that we believe may provide a viable patient-centric approach to aid clinicians in designing novel AI-based disease prediction strategies and monitoring disease progression. Our ultimate objective is to facilitate the future developments of AI in digital healthcare. This work proposes new data-driven machine learning-based solutions utilizing health data from multiple modalities, such as gait (e.g., spatiotemporal gait metrics, acceleration, ground reaction forces), cognitive, functional, and longitudinal clinical assessments, and neural responses measured via electrophysiological (e.g., electromyography (EMG), electrocardiography (ECG)) signals, to improve early disease prediction and progression in neurological movement disorders. We measure our ability to use these signals to classify disability and predict progression of cognitive and motor changes in persons with neuromuscular disorders. This thesis is a multidisciplinary effort that involves novel combinations of sensors, vision, machine learning, bio-mechanics, and dynamical analyses to better characterize neurological disorders. These studies on the integration of AI and health data may provide a viable patient-centric approach to aid clinicians in designing novel AI-based disease prediction strategies and monitoring disease progression. This may help providers to individualize treatment plans and design improved clinical trials; thus, help reduce the skyrocketing healthcare costs in the future. The focus of this dissertation is on the following three areas under the broad umbrella of AI for digital healthcare: 1) Gait analysis for differentiation of neurological disorders, where we focus on disease diagnosis and study gait data-driven methodologies for an automated quantification of neurological gait disorders, such as multiple sclerosis and Parkinson's disease, 2) Clinical data analysis for prediction of disease progression, where we focus on early-stage disease progression prediction and propose machine learning models to identify etiological disease subtypes and study trajectory progression in Alzheimer’s disease, and 3) Virtual reality for analyzing neural responses to anxiety, where we examine the potential of virtual reality neurorehabilitation for ameliorating fall-related anxiety in adults."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/117741"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Rachneet Kaur"],"dc:subject":["Artificial Intelligence","Deep Learning","Health Analytics","Gait","Vision","Multiple Sclerosis","Alzheimer's Disease","Parkinson's Disease"],"dc:title":["Machine learning for digital health: Transforming clinical data into knowledge"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Industrial Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}